# CoRect: Context-Aware Logit Contrast for Hidden State Rectification to Resolve Knowledge Conflicts

arXiv: https://arxiv.org/abs/2602.08221 · HTML: https://mlsanity.com/paper/2602.08221/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in RAG robustness and adaptive retrieval, one of 15 directions in Retrieval-augmented generation.
- First posted on arXiv 9 Feb 2026.

## Placed in

- Retrieval-augmented generation / [RAG robustness and adaptive retrieval](https://mlsanity.com/rag/rag-robustness-and-adaptive-retrieval/)

## Built on by (most cited first, 2 of 2)

- [FIDES: Faithful Inference via Deep Evidence Signals for Retrieval-Memory Conflict in RAG](https://mlsanity.com/paper/2606.05644/) (2026)
- [Contrastive Projection: Reading Transformer Internals by Differencing Logit Lenses](https://mlsanity.com/paper/2609.09902/) (2026)

## Abstract (arXiv)

Retrieval-Augmented Generation (RAG) often struggles with knowledge conflicts, where model-internal parametric knowledge overrides retrieved evidence, leading to unfaithful outputs. Existing approaches are often limited, relying either on superficial decoding adjustments or weight editing that necessitates ground-truth targets. Through layer-wise analysis, we attribute this failure to a parametric suppression phenomenon: specifically, in deep layers, certain FFN layers overwrite context-sensitive representations with memorized priors. To address this, we propose CoRect (Context-Aware Logit Contrast for Hidden State Rectification). By contrasting logits from contextualized and non-contextualized forward passes, CoRect identifies layers that exhibit high parametric bias without requiring ground-truth labels. It then rectifies the hidden states to preserve evidence-grounded information. Across question answering (QA) and summarization benchmarks, CoRect consistently improves faithfulness and reduces hallucinations compared to strong baselines.